There exist various video translation techniques for translating a video from one form to another, for example, one color tone to another color tone. However, most of the existing techniques use semi-automatic methods, requiring manual interactions. Of late, developments in artificial intelligence domain have paved way for implementing techniques in various systems, specifically trained systems, for complete automatic translation of a black-and-white video into a color video. However, such systems, for performing expected video translation, require intense training with various scenes/scenarios associated with different environments, objects, attributes, and actions along with the different possible colors of the scenes.
Further, there may be instances where an audio or speech content in the video refers to background or other coloring aspects of the video. For example, suppose one of the characters in the video utters a sentence such as—“the sky is Blue!”, looking at the sky. In such instances, although the scene lasts for only a few seconds in the video, the color of sky in the background of the scene is expected to have blue color, a little before and after the scene. However, most of the conventional video translation techniques transform the video without considering these necessities/contexts.
Also, most of the existing techniques, involve rigorous training process for generating metadata of the video, which is required for translating the video. However, such methods do not consider the audio and/or speech contents in the video, and hence fail to correlate between the audio and visual segments of the video. As a consequence, such systems fail to effectively perform translation of the video.
The information disclosed in this background of the disclosure section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.